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Standard Error of the Estimate of a Regression Line
The standard error of the estimate is a measure that tells us how close our values are from the regression line. In other words, it measures the accuracy of predictions. The lower the standard error of the estimate the better the predictions are.
When applied to the close series, we can measure how narrow the security trending channel is. This measure is also very useful when comparing regression lines of different securities or regression lines of different periods.
Example:
a = stdErrorEstimate(close, 50) > 1.5 * stdErrorEstimate(close, 25);
In the above example, I have calculated the standard error of the estimate of the 50-bar regression line and compared it to the standard error of the estimate of the shorter term regression line.
If the former estimate is 1.5 higher than the latter then returns 1. In other words, the above formula returns true when the short term trending channel becomes narrower than longer term trending channel.
To compare the standard error of estimation of two securities, you can use the "GetSeries" function.
Example:
A = stdErrorEstimate(close, 50) > stdErrorEstimate(GetSeries("GOOG", close), 50);
This will compare the error estimation of the analyzed security to the error estimation of Google.
Trading financial instruments, including foreign exchange on margin, carries a high level of risk and is not suitable for all investors. The high degree of leverage can work against you as well as for you. Before deciding to invest in financial instruments or foreign exchange you should carefully consider your investment objectives, level of experience, and risk appetite. The possibility exists that you could sustain a loss of some or all of your initial investment and therefore you should not invest money that you cannot afford to lose. You should be aware of all the risks associated with trading and seek advice from an independent financial advisor if you have any doubts.
