Linear Regression Indicator

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Description

The Linear Regression indicator is based on the trend of a security's price over a specified time period. The trend is determined by calculating a linear regression trendline using the "least squares fit" method. The least squares fit technique fits a trendline to the data in the chart by minimizing the distance between the data points and the linear regression trendline.
 
Any point along the Linear Regression indicator is equal to the ending value of a Linear Regression trendline. For example, the ending value of a Linear Regression trendline that covers 10 days will have the same value as a 10-day Linear Regression indicator. This differs slightly from the Time Series Forecast indicator in that the TSF adds the slope to the ending value of the regression line. This makes the TSF a bit more responsive to short term price changes. If you plot the TSF and the Linear Regression indicator side-by-side, you’ll notice that the TSF hugs the prices more closely than the Linear Regression indicator.

Rather than plotting a straight Linear Regression trendline, the Linear Regression indicator plots the ending values of multiple Linear Regression trendlines.

Interpretation

The interpretation of a Linear Regression indicator is similar to a moving average. However, the Linear Regression indicator has two advantages over moving averages.

Unlike a moving average, a Linear Regression indicator does not exhibit as much "delay." Since the indicator is "fitting" a line to the data points rather than averaging them, the Linear Regression line is more responsive to price changes.

The indicator is actually a forecast of the next periods (tomorrow’s) price plotted today. The Forecast Oscillator plots the percentage difference between the forecast price and the actual price. Tushar Chande suggests that when prices are persistently above or below the forecast price, prices can be expected to snap back to more realistic levels. In other words the Linear Regression indicator shows where prices should be trading on a statistical basis. Any excessive deviation from the regression line should be short-lived.