Linear regression is a mathematical model that is used to know coming price movement based on how the previous price movement was, and this is a statistical method that actually finds numerical values to get a trend. The liner regression trendline operates by plotting a straight line between the prices based on the least square method, which can be thought of the basic best fit line that grasps the best trend between a set of points. The linear regression angle indicator is responsible for plotting the trendline angle for each and every data point.
According to the straight line equation, (x1, y1), (x2, y2), . . . , (xn, yn) has the form y = mx + b, the best fit line with n points has the following equations:
Linear Regression Acceleration
To make the regression line more reliable, the selected data is smoothed to be more fit by a MA with the order of your preference. The finalized data will constitute the regression lines terminating at each bar. The change of the rate in the regression line from the rate of the previous line is known as linear regression acceleration. Then those values are normalized to be compared with a clear reference. Make sure you know the difference between comparing raw data and normalized data since both have distinctive meanings.
Acceleration = Change in Slope / Bar = Slope – Slope.1 Normalized Acceleration = Change in Normalized Slope / Bar = (Slope * 100 / Price) – (Slope.1 * 100 / Price.1)