rollsig turns financial time series into causal, rolling-window path-signature features, and slides the window at O(1) steady-state cost per tick using the group structure of the tensor algebra — Chen's identity and the group inverse. These three documents are the full record of why it is built the way it is, including the parts that did not work.
Steady-state per-tick cost is flat across a 120× change in window length, which makes it 129× faster than RoughPy at window 1200. Against compiled iisignature it wins only past a measured crossover, and the library's method="auto" encodes that rather than guessing. Numerical drift is measured, bounded, and priced.
On the Optiver Realized Volatility Prediction data, signature features did not improve on reproduced order-book baselines — on any of three pre-registered stock subsets, and not after the order-book state was handed to them as extra path channels. That line of enquiry is closed.