Brummel
67c1f51cfe
Sma::eval re-summed the whole window every tick — O(length)/tick, multiplied across millions of bars and every sweep point. Replace with the industry-standard incremental running sum (ta-lib shape): the node owns the window ring and keeps a running sum, adding the new sample and subtracting the evicted one each cycle, so the input column drops to depth 1 (lookbacks() = [1], like Ema). The running sum carries Kahan/Neumaier compensation (the fix pandas' rolling mean adopted) so it does not drift over long runs; Ema needs none — its recurrence is contractive. Determinism (C1) holds: same input -> same run, reproducibly. The float output differs from the full re-sum in the last ULPs (a new, deterministic baseline); the suite needed only one arity-assumption update (Sma lookback 3 -> 1), no equity golden changed. closes #39