Linear Regression Channels are useful measure for technical and quantitative analysis in financial markets that help identifying trends and trend direction. The use of standard deviation gives traders ideas as to when prices are becoming overbought or oversold relative to the long term trend
The basis of a linear regression channel
Linear Regression Line – is a line drawn according to the least-squares statistical technique which produces a best-fit line that cuts through the middle of price action, a line that best fits all the data points of interest. The resulting fitted model can be used to summarize the data, to predict unobserved values from the same system. Linear Regression Line then present basis for the channel calculations
The linear regression channel
2. Upper Channel Line – A line that runs parallel to the Linear Regression Line and is usually one to two standard deviations above the Linear Regression Line.
3. Lower Channel Line – This line runs parallel to the Linear Regression Line and is usually one to two standard deviations below the Linear Regression Line.
Unlike Fibonacci Channels and Andrew’s Pitchfork, Linear Regression Channels are calculated using statistical methods, both for the regression line (as expressed above) and deviation channels. Upper and Lower channel lines are presenting the idea of bell curve method, also known as a normal distribution and are calculated using standard deviation function.
A standard deviation include 68% of the data points, two standard deviations include approximately 95% of the data points and any data point that appears outside two standard deviations is very rare.
It is often assumed that the data points will move back toward the average, or regress and channels would allow us to see when a security is overbought or oversold and ready to revert to the mean
please note : Over time, the price will move up and down, and the linear regression channel will experience changes as old prices fall off and new prices appear
The basis of a linear regression channel
Linear Regression Line – is a line drawn according to the least-squares statistical technique which produces a best-fit line that cuts through the middle of price action, a line that best fits all the data points of interest. The resulting fitted model can be used to summarize the data, to predict unobserved values from the same system. Linear Regression Line then present basis for the channel calculations
The linear regression channel
2. Upper Channel Line – A line that runs parallel to the Linear Regression Line and is usually one to two standard deviations above the Linear Regression Line.
3. Lower Channel Line – This line runs parallel to the Linear Regression Line and is usually one to two standard deviations below the Linear Regression Line.
Unlike Fibonacci Channels and Andrew’s Pitchfork, Linear Regression Channels are calculated using statistical methods, both for the regression line (as expressed above) and deviation channels. Upper and Lower channel lines are presenting the idea of bell curve method, also known as a normal distribution and are calculated using standard deviation function.
A standard deviation include 68% of the data points, two standard deviations include approximately 95% of the data points and any data point that appears outside two standard deviations is very rare.
It is often assumed that the data points will move back toward the average, or regress and channels would allow us to see when a security is overbought or oversold and ready to revert to the mean
please note : Over time, the price will move up and down, and the linear regression channel will experience changes as old prices fall off and new prices appear
הערות שחרור:
remove unused parameter
הערות שחרור:
update default value
- default timeframe is 240 minutes (4h candle)
- default timeframe is 240 minutes (4h candle)