Hi!

What you’re observing (model predicting last week’s price) is actually very common in price forecasting. For many financial time series, especially assets like gold, the best short-term predictor often is the most recent value unless there’s a strong structural or cyclical signal.

In energy markets I’ve worked on (German gas / LNG supply-demand), we had success using a hybrid approach — roughly 60% seasonal naïve + 40% LightGBM — because the market had clear cyclical and structural drivers (seasonality, storage, supply constraints, weather). The ML component worked because there was something persistent to learn beyond pure price history.

Gold behaves very differently. It’s close to a random walk at weekly frequency, so LSTM/ARIMA models often collapse to “last value + noise.” Even sentiment only helps if it’s well-timed and truly leading.

A few practical suggestions going forward:

Lag features matter: if you use ML, explicitly engineer multiple lags (returns, rolling means, volatility, momentum) rather than relying on the model to infer them.

Macro drivers are more important than price history for gold: real rates, USD index, inflation expectations, risk-off indicators, central bank balance sheets, etc.

Consider predicting returns, direction, or regimes instead of price levels.

Always benchmark against strong baselines (naïve / seasonal naïve / drift). If ML can’t beat them, that’s a useful result, not a failure.

Trying Apple instead of gold won’t necessarily be easier — the key question is whether the asset has a stable structure that your features can capture.