I had really mixed feelings after reading this article because on one hand, I was beaten to the punch (I was already working on using Twitter trends to predict

I had really mixed feelings after reading this article because on one hand, I was beaten to the punch (I was already working on using Twitter trends to predict how stocks would change), but on the other hand, I am very happy to know that the idea was actually proven to work, after I toiled for so many hours determining how it might actually work. The best solution that I found was to factor the rise in rate in positive or negative twitter posts in a given day into an algorithm already based on something called the "magic formula". The "magic formula" is formed on the thinking that stocks can be chosen based on their ranking in a list; this ranking is determined by a high return on capital, low p/e ratio, and a high volume. My plan was to factor in twitter trends to the list so that stocks would be ranked better, based on public opinion of the stock. I actually was able to figure out how to do this relatively well, as I improved the accuracy of the "magic formula", but failed to make it significantly better. The key is to study the trends and figure out when stocks have a lot of negative talk about them, and then buy them right as the rate of negative tweets about the company stops. Would anyone be interested in developing this idea further for open source use or even possibly for some profit?