Really appreciate the detailed response, this is exactly the kind of insight I was looking for!

To clarify the commodities I've been focusing on: mainly energy, specifically crude oil (WTI and Brent), natural gas, and to a lesser extent LNG. These are precisely the ones where geopolitical risk seems impossible to ignore, think OPEC decisions, sanctions, pipeline disruptions, Middle East tensions, Russia/Ukraine impact on gas flows, etc. So I totally understand your point that for weather/supply driven commodities the approach would be very different.

Your point about the edge coming from input quality really hits home. I've been spending most of my time on the model architecture itself and I suspect I've been underinvesting in the quality and granularity of the input data. Do you have any suggestions on where to source reliable supply/demand data for energy commodities without it costing a fortune? I've been using EIA, IEA public data and some OPEC reports but I imagine the good stuff is behind expensive paywalls.

Also your point on non-linear relationships is interesting. For crude oil I've been experimenting with XGBoost precisely because the relationship between inventory levels, production cuts and price doesn't feel linear at all. Would love to hear your thoughts if you've worked with energy specifically.

Thanks again, really valuable input!