Predicting gold price fluctuations using artificial intelligence algorithms in line with technical volatility; a case study of the Iranian market

In this study, we present a novel hybrid strategy for short-term gold market trading in Iran using artificial intelligence. The proposed framework combines Long Short-Term Memory (LSTM) networks and Support Vector Machines (SVM) to forecast gold price trends and generate buy/sell signals. Utilizing five years of real-time data (April ۲۰۲۰ – April ۲۰۲۵), including daily gold prices, exchange rates, and technical indicators such as RSI, MACD, MA, and Bollinger Bands, we demonstrate that the LSTM model significantly outperforms SVM and Random Forest in terms of prediction accuracy. Our results highlight the effectiveness of deep learning in modeling non-linear patterns in the Iranian gold market, offering a practical and accurate decision-making tool for technical traders.

کلیدواژه ها:

Volatility ، Artificial Intelligence ، LSTM ، Gold Market ، Machine Learning ، MATLAB ، Iranian Market ، Technical Analysis