Adaptive Tracking Filter Based on Price and Range Uncertainty

Summary

The document introduces an adaptive price filter through an intuitive explanation of Kalman filtering. It starts from the idea of a moving average and describes adjusting the filter’s responsiveness to balance two sources of uncertainty: how much price is moving and how reliably that movement can be measured. Its example uses the midpoint of each bar’s high and low, smooths price change and bar range, and uses their relative size to set the filter’s smoothing factor. The stated aim is a moving average with little lag that tracks changing prices.

The document supplies a formula and an implementation example, but no empirical results, benchmark comparison, or trading rules. It explicitly presents the market application as less rigorous than a full statistical treatment and says rigorous use would require knowledge of the relevant probability distributions. The filter is therefore a signal-processing concept, not evidence of a profitable strategy; its behavior and usefulness would need to be tested on the intended market and data.

Key ideas

• The filter adapts its smoothing to the relative size of price movement and the bar’s high-low range.
• It applies the adaptive smoothing factor to the bar midpoint to produce a tracking series.
• The approach is presented as an intuitive path from moving averages to Kalman-style estimation.
• The document cautions that rigorous application requires modeling the statistics’ probability distributions.
• No backtest or evidence of trading profitability is provided.

Tags

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