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RollingBetaShiftDetector

Incremental, causal technical analysis documentation

Summary

RollingBetaShiftDetector is RTTA's streaming implementation of: Causal adjacent-window beta shift detector.

Update API

result = rtta.RollingBetaShiftDetector(window=20, threshold=0.25).update(real0, real1)

The update(...) call consumes one observation using real0, real1. advance(...) uses the same inputs when the caller wants to update state without materializing a Python return value.

Theory Of Operation

RollingBetaShiftDetector 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 \(z_t = (real0_t, real1_t)\) denote the observation consumed by one update(...) call and let \(\theta\) denote constructor parameters such as window lengths, thresholds, and smoothing constants.

\[\beta^R_t=\frac{\operatorname{cov}(R^x_t,R^y_t)}{\operatorname{var}(R^y_t)}, \qquad \beta^B_t=\frac{\operatorname{cov}(B^x_t,B^y_t)}{\operatorname{var}(B^y_t)}\]
\[q_t=\beta^R_t-\beta^B_t\]
\[r_t = \begin{cases} 1, & q_t > h \\ -1, & q_t < -h \\ 0, & \text{otherwise} \end{cases}\]

The return value is the current scalar indicator value.

Implementation Notes

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

Reference