Standard Error¶
| Name | Type | Prerequisite | Use Cases |
|---|---|---|---|
| Standard Error (SE) | Volatility/Statistics | StdDev | Gauging the consistency of a trend. |
Definition¶
Standard Error measures the statistical accuracy of the linear regression estimate. It measures the dispersion of the price data around the linear regression line.
Mathematical Equation¶
\[
SE = \sqrt{ \frac{\sum (y_i - \hat{y}_i)^2}{N} }
\]
Where \(y_i\) is the actual price and \(\hat{y}_i\) is the predicted price from the regression line.
Special cases¶
- Maximum possible value: Unbounded
- Minimum possible value: 0
- Behavior: Moves independently, representing the error size when trying to fit a linear regression.
Visualization¶
Trading Significance¶
-
Trend Reliability: Low Standard Error indicates that prices are clustering closely to the regression line, suggesting a strong, reliable trend.
-
Volatility: High Standard Error indicates high volatility and a less reliable trend structure.