SpreadFeatures

Summary

SpreadFeatures is RTTA's streaming implementation of: Quoted, effective, and realized spread estimates from trades and contemporaneous quotes.

Update API

result = rtta.SpreadFeatures().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

SpreadFeatures combines price, volume, and/or quote information into a streaming microstructure or participation measure. The update path advances only from the latest tick and prior state.

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.

\[mid_t=\frac{bid_t+ask_t}{2}, \qquad spread_t=\max(ask_t-bid_t,0)\]

\[relative\_spread_t=\frac{spread_t}{\max(|mid_t|,\epsilon)}, \qquad trade\_location_t=\frac{trade_t-mid_t}{\max(spread_t,\epsilon)}\]

update(...) returns a result struct with fields quoted_spread, effective_spread, realized_spread.

Implementation Notes

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

Reference

Background reference