Test your stock-picking skills with instant feedback. Most common word from my users: "addicting".

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Then you don't have to use image processing (including actually reading the image), and they'd have to become right-click-and-copy-paste-image-src-check-in-new-tab-cheater! :-)

If everyone looking at a chart determines that the exit price should be $20 (let's assume the stock has shown a resistance level at that price) many people would try to exit around $20. The assumption would then be that if everyone does indeed believe that $20 is a ceiling, then everyone would sell at $20 thus creating the ceiling.

I believe sound fundamental analysis is the optimal method of analysis, but even that method has been called into question over the course of the last eight months as many investors are learning that much of what is on the books is either someone's best guess or flat out fraudulent at best.

The main problem right now is that since the market is long-biased and there's no concept of time in the rankings, it's easy to get to the top by always clicking "Buy". It would be cool if you could make the rankings actually reflect a confidence score of how well a user is beating the market.

I'm not very strong on the statistics, but here's a possibility: for each trade, calculate the user's excess return against the market, represented by an index fund: (user's return % - market return %), and multiply that by the number of days. Then assign a confidence score using a t-test, maybe: t = (excess return * days) / (sample variance / sqrt(number of days)), and look up the confidence level based on the t-value. Finally, rank users according to their confidence level.

Something else--since this is technical analysis, it would be cool if you could include some other indicators on the chart, e.g. RSI, more than one moving average, bollinger bands, etc. That could give you some cool metrics--you could track how well each combination of indicators improves picks, by user and in aggregate.

Real statistical arbitrage firms, as I understand it, work by looking at the historical variation in thousands of asset prices at once, putting them into an enormous covariance matrix, and using dozens of very smart mathematicians and millions of CPU hours to figure out which assets are likely to be currently underpriced.

That's how folks like Renaissance Technologies got rich. Unfortunately, as soon as the secret got out, other folks started doing it as well, and pretty soon the mispricing of assets got smaller and smaller, so they're in an arms race to find more and more sophisticated algorithms.

So I guess the point is that if mispriced assets are that hard to find when you have a hundred PhDs and a supercomputer, you're not likely to do very well just by looking at whether a squiggly line is currently above or below its recent average. All the easy money has already been vacuumed up.