Linear Regression

The interpretation of the Linear Regression Indicator is to forecast tomorrow’s price by plotting today’s price, with the expectation for the prices to return to the realistic levels even if the price is higher or lower than the forecasts. The Linear Regression Indicator shows where the prices “should” be trading on a statistical basis.

The Linear Regression Indicator schedules the trend of a market instrument’s price on consecutive days. A calculation of the Linear Regression Trendline with the “least square fit” method defines this trend and helps to minimize the distance between the data point and a Linear Regression Trendline.

The trend identification and trend following is similar to moving averages using the linear regression indicator. The regression line is a straight line fitted to a series of data points and is not the same as the indicator. The benefit of linear regression indicator over a normal moving average is that it reacts faster to directional changes due to it having less lag than the moving average.

Blue lines indicate Regression indicator

Step 1: Make a chart of your data, filling in the columns in the same way as you would fill in the chart if you were finding a Correlation Coefficient.

Step 2: Use the equations:

Where y = a + bx

Step 3: Insert the values into the equation.


  • If the current price is above the linear regression line it suggests that the price is unrealistically high and hence the price will come down soon – time to sell.
  • Where the current price is below the line then the price is too low so the expectation is the price will rise – time to buy.