The intrinsic value of metals provides a stable hedge against inflation; however, various macroeconomic factors, such as inflation, interest rates, and money supply, can have a profound impact on metal prices. This paper proposes a deep learning model to predict the prices of non-ferrous and precious metals, given their importance as components in investment portfolios. Positional encoding was applied to encode the daily insights of the sparse macroeconomic data, and a dynamic correlation encoder was used to model asset relationships without predefined information. In experiments, the proposed framework outperformed state-of-the-art methods in terms of Mean Absolute Error (MAE).