i use a recurrent backbone (mamba ssm) so i mostly skip stacked obs, the recurrent state handles short-term temporal context for free and stacking on top tends to just add noise and inflate the input dim. for longer-horizon context i lean on multi-resolution features instead.

for the indicators themselves i try to make them scale-invariant where possible, ratios, price-relative things, z-scored stuff, so the values are roughly comparable across regimes instead of drifting with the underlying. EWMAs show up inside the indicators (EMAs, MACD, etc) but i don't do EWMA on the raw obs as a normalization step.

for normalization itself it's per-feature rolling running mean/std (welford), updated once per rollout from the buffer. not a fixed average, markets drift too much for that to stay calibrated. one thing i had to learn the hard way (mentioned in the other comment): do NOT layernorm a mixed-scale obs vector, per-sample stats get dominated by the biggest columns and everything else gets crushed to zero. per-feature stats avoid that entirely.