Implementing Alpha191 Count and Regression Functions

Summary

This note explains code equivalents for three functions associated with the Alpha191 factor set. Count(a, n) is illustrated by converting a condition—whether the current close exceeds the previous close—into 1 or 0, then summing those values over a rolling window. The example uses five observations, so the result counts how often the condition held during that span.

Regbeta(a, b, n) is described as a rolling regression coefficient, illustrated by estimating the coefficient of returns against closing price with a beta function. Regresi is then formed by subtracting the coefficient multiplied by closing price from returns. The examples convey the intended calculations but do not specify regression conventions, edge-case handling, or empirical performance. The page labels these as a legacy implementation for learning, so the expressions may need adaptation for newer platform interfaces.

Key ideas

• A rolling count can be computed by mapping a condition to binary values and summing them over a window.
• The example counts sessions when the current close is higher than the previous close.
• The regression beta example estimates returns against closing price over a rolling window.
• The residual-like expression subtracts beta times closing price from returns.
• The page identifies the implementation as legacy and provides no performance evaluation.

Tags

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