I tend to avoid time series forecasting when I can help it because I find it hard to communicate to stakeholders that a neural network (or another method) is not an oracle.

If you are talking about granularity of observations, it would depend on what you are trying to predict (the price in an hour or the price in 12 months?) and how quickly you need the prediction (100ms? Tomorrow morning?). If I had infinite data I would use granularity as a hyper parameter and tune that to a level that produced the best test results.

I am for example currently using weekly averages for non-price data forecasting. I could use daily data but weekly is absolutely adequate for this purpose.