If you’re looking for information on moving linear regression and how it can help you make trading decisions, check out this insightful guide!

  1. How Moving Linear Regression is Used
  2. Conclusion

Moving linear regression is a trend following indicator which plots a dynamic form of the linear regression indicator.

Linear regression involves accumulating numerous data points in a sample before bringing a ‘best fit’ line to reflect the data’s general trend. A linear regression line could still point down if a market is up across a specific timeframe, and vice versa.

That’s because the majority of linear regression forms are based on the average (or mean), which makes them sensitive to anomalies.

As an example, if we studied the movement of a market over a long enough timeframe, with price rising and falling, we might see that the market was up in price in relation to where the linear regression line started and stopped.

The moving linear regression would display these lines’ flow over time, and their endpoints would always meet if they were plotted on the same period. It’s a simple system to understand once you know what you’re looking for.

How Moving Linear Regression is Used

Moving linear regression might bear some visual similarities to moving averages, but the calculation process is quite different.

An average of closing prices are used for calculating moving averages, as with simple moving averages (SMA).

On the other hand, the moving averages calculation can be performed by applying more substantial weights to prices that are more recent, and lower weights to prices from further in the past — and averaging them together (here, the most common form is the EMA, or exponential moving average).

But moving linear regression joins a continuous run of linear regression line endpoints together instead.

When we consider these contrasting methodologies, we can say that moving linear regression typically hugs price much more closely than moving averages of equal periodicity. If we were to study a market chart, we might see that the moving linear regression line deviates from price where volatility and fast changes in the trend occur.

This is especially the case on longer timeframes, and the line will need a little more time to catch up to price. It will when the market goes back to moving in a specific direction, whether it’s down, up, or sideways.

Moving linear regression may be thought of as a moving average substitute, and it’s most effective in trend following systems (as with EMAs and SMAs). Moving linear averages can offer insight into the direction, rate of change, and magnitude of a trend. Trend and momentum traders can bear all of these out with moving linear regression.

But for those trading price reversals instead, a crossover strategy using moving linear regression may be viable — it could actually be preferable based on how moving linear regression has greater responsiveness to price. Relative to moving averages, reversals may be spotted sooner.


So, what is moving linear regression? It’s a trend following indicator plotting a dynamic form of the linear regression indicator, used to track a trend by matching data through taking the ‘best fit’ line. This is instead of using basic or weighted averages, as with moving averages.

Moving linear regression could be utilized as an alternative version of the moving average, as they’re similar in concept in terms of what they aim to capture. This can assist traders in understanding what direction a trend is moving in, its magnitude, and its rate of change.

Moving linear regression is especially helpful for traders who want to stay aware of a prevailing trend in a market in which they trade. Those who trade reversals can also benefit from using crossovers as a way to spot opportunities.

But remember: a trader should never utilize an indicator on its own, whatever it is, to make trading decisions. Moving linear regression is best used as one element of a wider system for guiding choices, along with alternative indicators, price analysis, and/or fundamental analysis.