Variance-Weighted Regression Trends for Volatility-Adaptive Channels

Summary

The document explains a regression trend indicator that gives less influence to bars with high local residual variance. It first fits an ordinary least squares line, smooths squared residuals to estimate local variance, adds regularization, and converts variance into normalized, clipped weights. A weighted fit then supplies the trend slope, channel measures, fit quality, and effective sample size. The text compares variance smoothing choices and describes optional trend quality gating, channel expansion, and a forward projection display.

Synthetic examples show that weighting changes the line most in windows combining quiet and noisy regimes, while homogeneous windows tend toward ordinary regression. The author also reports reduced trend flips from a quality gate, but cautions that price residuals are autocorrelated, making the classical slope standard error too optimistic for significance testing. The indicator is presented as a trend filter, adaptive envelope, and regime aid; the document offers synthetic illustrations, not live trading validation, and its configuration section is truncated.

Key ideas

• Bars receive inverse-variance weights based on smoothed squared residuals from an initial linear fit.
• Regularization and weight clipping prevent extreme weights and keep the weighted fit stable.
• The effective sample size indicates how many bars meaningfully influence the fitted line.
• Weighting tends to matter most when a window mixes calm and volatile regimes.
• The quality gate can reduce flips, but autocorrelated residuals undermine classical significance interpretations.
• The channel and projection are analytical aids, and the text does not establish live trading performance.

Tags

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