# Linear Regression Channel

Linear Regression Channel is a three-line technical indicator used to analyze the upper and lower limits of an existing trend.

Linear regression is a statistical tool used to predict the future based on past data. It is used to determine when prices may be overextended.

Linear regression channels provide potential buy and sell signals based on price fluctuations.

consists of three parts:

1. Linear regression line
2. Upper channel line
3. Lower channel line

### Linear regression line

A Linear Regression Line is the straight line that best fits the price between the starting price point and the ending price point.

"Best fit" means constructing a line where the space between the price point and the actual linear regression line is minimal.

The

Linear Regression Line is used to determine the trend direction.

acts as the midpoint of the trend.

Think of the trendline as the "equilibrium" price, any movement above or below the trendline indicates overzealous buyers or sellers.

When price deviates above or below this line, you can expect price to return to the linear regression line.

When the price is below the linear regression line, it is considered bullish.

When the price is above the linear regression line, this is considered bearish.

### Upper and lower channel lines

The

upper channel line is a line parallel to the linear regression line, usually one to two standard deviations above the linear regression line.

It marks the peak of the trend.

The

lower channel line is a line parallel to the linear regression line and is usually one to two standard deviations below the linear regression line .

It marks the bottom of the trend.

The upper and lower channel lines are evenly spaced from the linear regression line

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The default standard deviation setting used is "1", which means that 68% of all price movements are contained between the upper and lower channel lines,

Buy and sell signals are generated when price breaks out of the channel.

## Types of linear regression channels

There are two types of linear regression channels, depending on the direction of the trend:

1. Bullish Linear Regression Channel
2. Bearish Linear Regression Channel

Both types of regression channels are defined based on their slopes .

### Bullish Linear Regression Channel

The bullish linear regression channel indicates a bullish trend. Prices are increasing and the slope of the linear regression is positive.

### Bearish Linear Regression Channel

Bearish linear regression channel indicates a bearish trend. Prices are falling and the slope of the linear regression is negative.

## How to plot a linear regression channel

To draw a linear regression channel, simply select the starting point of the trend and stretch the indicator to another point in the trend.

The three lines of the linear regression channel will adjust themselves based on the tops and bottoms of the trend.

The linear regression channel (midline) will automatically appear between the upper and lower channels.

## How to use linear regression channels

Trading linear regression channels requires paying close attention to the interaction of price with one of the three lines.

Every time price interacts with an upper or lower channel, you should expect to see potential turning points on the price chart.

If you expect the trend to continue and the price breaks below the lower channel line, this should be considered a buy signal.

You can wait for confirmation by waiting for price to move higher and close back within the linear regression channel.

### Sell signal

If you expect the trend to continue and the price rises above the upper channel line, you should consider it a sell signal.

You can wait for confirmation by waiting for the price to move lower and retrace within the linear regression channel.

### Trend reversal

When price closes outside of a linear regression channel for an extended period of time, this is often interpreted as an early signal that the current trend may be ending and a trend reversal may be imminent.

### Overbought/Oversold

Using standard deviation can give you an idea of ​​when a price may be overbought or oversold relative to the long-term trend.