TTP Pair Slope/HedgePair slope/hedge uses linear regression to calculate the hedge ratio (slope) between the two assets within a period.
It allows you to specify a "from" and a "to" candle.
Example:
"A regression from 1000 candles back in time and ignore the last 100 candles. This would result in making a regression of 900 candles in total."
The formula used to perform the regression with the assts X and Y is:
Hedge =
mean( (X-mean(X))^2 )
——————————————————
mean( (X-mean(X)) * (Y-mean(Y)) )
You can later use the hedge in a chart of X - Hedge * Y
(Confirm with 1 / hedge )
If the plot is stationary the period tested should look like stationary.
If you cross an imaginary horizontal line across all the values in the period used it should look like a flat channel with values crossing above and below the line.
The purpose of this indicator is to help finding the linear regression test used for conintegration analysis. Conintegration assets is one of the requirements to consider assets for pair and hedge trading.
Linearregressionslope
Cycle-Period Adaptive, Linear Regression Slope Oscillator [Loxx]Cycle-Period Adaptive, Linear Regression Slope Oscillator is an osciallator that solves for the Linear Regression slope and turns it into an oscillator. This is a very simple calculation and uses one of Ehler's first implementations of his cycle period calculations. The output slope value is smoothed after calculation and before being drawn. This is a sort of momentum indicator and has a rich history with Forex traders around the world.
What is the Cycle Period?
The spectral content of the data are measured in a bank of contiguous filters as described in "Measuring Cycle Periods" in the March 2008 issue of Stocks & Commodities Magazine. The filter having the strongest output is selected as the current dominant cycle period. The cycle period is measured as the number of bars contained in one full cycle period.
What is Linear Regression?
In statistics, linear regression is a linear approach for modeling the relationship between a scalar response and one or more explanatory variables. The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression.
Included:
Bar coloring
2 signal types
Alerts
Loxx's Expanded Source Types
Loxx's Moving Averages
