Reconstructing the RSRS Indicator with a Rolling Price Regression

Summary

This short Q&A describes how to reproduce the RSRS indicator in the BigQuant environment. It defines the indicator as the slope, or beta, from a linear regression using the high and low prices over a preceding window of N days. The resulting slope is the RSRS value, and the post says it can be built with the platform’s factor-expression tools.

The answer gives the core construction concept but does not specify the regression orientation, window length, any standardization or scoring variant, or how the signal should translate into trades. It includes links to a demonstration video and shared source, but the text itself reports no backtest, trading performance, or evidence that a particular parameterization works. The description is therefore a starting point for recreating the indicator rather than a full strategy or validation guide.

Key ideas

• RSRS is described as the regression slope between recent highs and lows over an N-day window.
• The slope coefficient, beta, is treated as the indicator value.
• The factor can be constructed with BigQuant’s factor-expression functionality.
• The post does not specify window parameters, signal rules, or empirical performance.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.