TrendChopRegimeDetector

Summary

TrendChopRegimeDetector is RTTA's streaming implementation of: Efficiency-ratio trend-versus-chop regime detector using true range.

Update API

result = rtta.TrendChopRegimeDetector().update(close, high, low)

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

Theory Of Operation

TrendChopRegimeDetector first constructs a scalar market-state metric from the current observation and compact streaming state, then passes that metric through explicit entry/exit hysteresis. The metric is named in the recurrence below; the hysteresis keeps the output stable until the metric crosses the opposite exit band.

Recurrence

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

\[TR_t=\max(high_t-low_t,\ |high_t-close_{t-1}|,\ |low_t-close_{t-1}|)\]

\[q_t=\frac{|close_t-close_{t-n}|}{\sum_{i\in W_t}TR_i}\]

The metric is an efficiency ratio: values near one indicate directional trend, while values near zero indicate choppy movement.

\[r_t = \begin{cases} 1, & r_{t-1} \le 0 \text{ and } q_t \ge u_e \\ 0, & r_{t-1} = 1 \text{ and } q_t \le u_x \\ -1, & r_{t-1} \ge 0 \text{ and } q_t \le \ell_e \\ 0, & r_{t-1} = -1 \text{ and } q_t \ge \ell_x \\ r_{t-1}, & \text{otherwise} \end{cases}\]

The entry/exit constants satisfy \(\ell_e < \ell_x \le u_x < u_e\).

The return value is the current scalar indicator value.

Implementation Notes

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

Reference

Background reference