ExecutionCostSlippageRegimeDetector

Summary

ExecutionCostSlippageRegimeDetector is RTTA's streaming implementation of: Stateful relative execution-cost/slippage regime detector from trade price versus quote mid.

Update API

result = rtta.ExecutionCostSlippageRegimeDetector().update(trade_price, bid_price, ask_price)

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

Theory Of Operation

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

\[mid_t=\frac{bid_t+ask_t}{2}, \qquad q_t=\frac{|trade_t-mid_t|}{\max(|mid_t|,\epsilon)}\]

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

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 ExecutionCostSlippageRegimeDetector.

Reference

Background reference