Onset Trend Detector Study

By Samantha Baltodano

TL;DR:

The Onset Trend Detector is a tool for analyzing trends in stock data. Its main purpose is to remove the lag effect.

It was created by John F. Ehlers and uses a combination of the Ehlers Roofing Filter and the Super Smoother Filter to remove certain types of data from stock information. This helps to eliminate repeating patterns that are longer than a certain time period and make the data clearer and easier to analyze for trends. 

What Is the Onset Trend Detector Strategy?

The Onset Trend Detector study is a trend analyzing technical indicator developed by John F. Ehlers, based on a non-linear quotient transform (don’t worry about understanding how to calculate that just yet, I’ll share a nifty tool to test this strategy). Two of Mr. Ehlers' previous studies, the Super Smoother Filter and the Roofing Filter, were used and expanded to create this new complex technical indicator. 

Being a trend-following analysis technique, its main purpose is to address the problem of lag that is common among moving average type indicators.

How Does It Work

The Onset Trend Detector uses a special filter called the Ehlers Roofing Filter to remove certain types of data from stock information. This helps to eliminate repeating patterns that are longer than a certain time period, which is usually set to at least 100 days by default but can be changed. These long lasting, repeating trends are known as spectral dilation. By removing spectral dilation, you make the data clearer and easier to analyze for trends.

Filtered data is then subjected to re-filtering by the Super Smoother Filter so that the noise (cyclic components with low length) is reduced to a minimum. The period of 10 bars is a default maximum value for a trend cycle to be considered noise; it can be customized as well. 

Once the data is cleared of both noise and spectral dilation, the filter processes it with the automatic gain control algorithm which is widely used in digital signal processing. 

This algorithm registers the most recent peak value and normalizes it; the normalized value slowly decays until the next peak swing. The ratio of previously filtered value to the corresponding peak value is then quotiently transformed (again, don’t worry too much about these calculations) to provide the resulting oscillator. 

The quotient transform is controlled by the K coefficient: its allowed values are in the range from -1 to +1. K values close to 1 leave the ratio almost untouched, those close to -1 will translate it to around the additive inverse, and those close to zero will collapse small values of the ratio while keeping the higher values high.

Indicator values around 1 signify uptrend and those around -1, downtrend.

Test The Onset Trend Detector

Great news! 

You can back test this exact strategy on historical data for any of your favorite symbols using TradingView. 

This strategy has already been built and all you have to do is log in and take it for a spin. You can access this indicator here.

If you’re new to back testing and to TradingView, don’t worry. I created a step-by-step guide you can follow to begin testing the Onset Trend Detector.

Summary

• The Onset Trend Detector is a trend analyzing technical indicator developed by John F. Ehlers to address the problem of lag that is common among moving average type indicators.
• It uses a special filter called the Ehlers Roofing Filter to remove certain types of data from stock information, eliminating repeating patterns that are longer than a certain time period, which is usually set to 100 days by default but can be changed.
• The filtered data is then subjected to re-filtering by the Super Smoother Filter to reduce the noise (cyclic components with low length) to a minimum.
• The indicator values around 1 signify uptrend and those around -1, downtrend.

Onset Trend Detector is just one of many studies that Archaide automates. For a full list of strategies and studies available click here.

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