I was reading some of my other trading blogs and I came across a post over at TraderDNA that describes some of the metrics that professional traders use to improve their performance and gauge their success. In my own trading portfolio, I have a few metrics including % gain, buy price, sell price, % retracement, expectancy, average win, average loss and a few more. This web page has a ton more things to track, including Time in Winners, Time in Losers, Time since last Win, Time since last Loss, Average Profit/Loss, and plenty more. I hadn’t really considered any of those metrics before, but obviously if I pay attention to them, I can use them to improve my trading.
It also gave me an idea for spam effectiveness. In order to become the best in spam filtering, a solution needs more than block the most spam. They need to measure a whole stack of indicators and work on improving them all. I think looking across the following metrics would provide a cohesive set of indicators that, when improved, will greatly enhance the user experience:
– spam filtering % (general and per domain)
– false positive % (general and per domain)
– time to identify a new spam
– time to respond to new spam
– time to release false positives
– time to release blacklisted IPs
– spam-in-the-inbox (a measure of spam on the user experience)
– time-to-detect a new IP as spammy
– time-to-detect a formerly spammy IP as clean or dormant
– spam filtering % against different classes of spam (phishing, pharmaceutical, stock spam, image spam, etc)
– time-to-detect a general spam outbreak
– time-to-detect a localized spam outbreak
There’s probably a whole bunch more, but I think that will do for a start. I think that’s the key for an anti-spam solution to become best in class.