RollingMeanShiftDetector

Summary

RollingMeanShiftDetector is RTTA's streaming implementation of: Causal adjacent-window mean shift detector using a two-sample z-score.

Update API

result = rtta.RollingMeanShiftDetector(window=20, threshold=3.0).update(close)

The update(...) call consumes one observation using close. advance(...) uses the same inputs when the caller wants to update state without materializing a Python return value.

Theory Of Operation

RollingMeanShiftDetector compares two adjacent rolling windows: a reference window and a recent window. The C++ state moves expired recent samples into the reference window, maintains sufficient statistics, and emits the sign of the statistic difference when it exceeds the configured threshold.

Recurrence

Let \(z_t = close_t\) denote the observation consumed by one update(...) call and let \(\theta\) denote constructor parameters such as window lengths, thresholds, and smoothing constants.

\[\bar{x}^{R}_t,\sigma^{2,R}_t=\operatorname{stats}(R_t), \qquad \bar{x}^{B}_t,\sigma^{2,B}_t=\operatorname{stats}(B_t)\]

\[q_t=\frac{\bar{x}^{R}_t-\bar{x}^{B}_t} {\sqrt{\sigma^{2,R}_t/n+\sigma^{2,B}_t/n+\epsilon}}\]

\[r_t = \begin{cases} 1, & q_t > h \\ -1, & q_t < -h \\ 0, & \text{otherwise} \end{cases}\]

The return value is the current scalar indicator value.

Implementation Notes

The recurrence is implemented in src/rtta/indicator.cpp in class RollingMeanShiftDetector.

Reference

Background reference