An analytical framework for real-time gold trading using sentiment and time-series forecasting

Abstract

Predicting financial markets remains a significant challenge due to their volatility and the complex interplay of various factors influencing price movements. This study presents a method named AchillesV1¹, which is a hybrid long short-term memory (LSTM) neural network model designed to predict the Gold vs. USD exchange rate in real time. The model is trained on high-frequency time-series data and incorporates technical indicators such as the Relative Strength Index (RSI) and Exponential Moving Average (EMA) to enhance prediction accuracy. Additionally, we integrate a FinBERT-based sentiment analysis to evaluate financial news sentiment, further refining trading decisions. The predictions are utilized within an automated trading bot, which executes buy and sell orders based on market conditions. A month-long backtesting experiment (excluding weekends) demonstrated approximately 184% net profit, indicating the model’s effectiveness in live trading scenarios. This work highlights the potential of machine learning in financial markets and contributes to the growing body of research on AI-driven algorithmic trading strategies. Additionally, this trading bot is adaptable to other platforms, including stock and cryptocurrency trading platforms.