You could restrict your definition of "trendiness" to mean "linearity".
Then you could fit a linear regression best-fit line to prices. One result of the linear regression fit is "R squared", the coefficient of determination. The higher the R-squared, the better the fit. The more linear are prices. The more trendy are prices. (Fire up Microsoft Excel and read the Help article for "LINEST" to learn more)
Or you might be interested in "Profitable Trendiness" rather than just trendiness. A strong trend up, with perhaps a bit of noise, might be preferable to a less strong trend with less noise.
In this case you might be interested in (Linear Regression Slope) x (Coefficient of Determination R Squared).
To compare one instrument against another, you may want to normalize "Slope" by the close, or by the volatility. Otherwise you'll always get the same answer: Berkshire Hathaway has got the steepest slope of all stocks. Which is true but not helpful. (Raw slope is measured in points per day. Berkshire Hathaway slopes more points per day than any other stock.)