Rolling Regression Methods and Diagnostics for Trading Data

Summary

This Pine Script library implements rolling regression tools for modeling a target series from as many as four predictors. Its methods include ordinary least squares, Ridge, Lasso, Elastic Net, logistic, robust Huber, and quantile regression. The accompanying example fits price using volume and RSI, compares ordinary and robust fits, plots quantile estimates as a band, and reports diagnostics such as R-squared, residual error, coefficient statistics, and p-values.

The toolkit retrains over a trailing window on each bar, standardizing features for several optimization routines and using iterative procedures for penalized, robust, logistic, and quantile fits. This makes computation scale with window length and iteration counts; the document advises keeping these settings modest when processing many bars or making multiple calls. The example is illustrative rather than a trading evaluation: it reports no predictive or strategy performance, and regression estimates, diagnostics, and quantile bands do not by themselves establish out-of-sample value or causal relationships.

Key ideas

• The library offers rolling linear, penalized, logistic, robust, and quantile regression methods.
• Several methods accept up to four predictors and return fitted values and coefficients.
• Regularization can constrain coefficients, while robust regression reduces the influence of outlier observations.
• Quantile regression provides conditional estimates at chosen quantiles rather than only a single fitted value.
• Each bar triggers a new window fit, so computation rises with the lookback and iterative solver settings.
• The example demonstrates model comparison and diagnostics but does not establish trading performance.

Tags

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