I realize that it is kinda unfair to criticize the performance, since you say up front:
NOTE: This algorithm is not intended to perform consistently over all time periods. Ranking schemes have predictive lifecycles, and the goal of someone trying to use this algorithm to trade profitably now should be to find a predictive ranking scheme that will work in the future.
However, I find it a bit odd that Q has made a big investment in the pipeline API, with the idea that it would support viable factor-based long-short algos for the fund, but you've yet to write some working examples (meaning ones that could be funded). And even before developing the API, how did you get the confidence that lots of unique strategies could be deployed profitably on Q by a large number of users (it is intended to be a crowd-sourced effort, so a handful of long-short algos in the fund won't cut it)?
Simon seems to have pretty good intuition, so when he says "it's an exceedingly tough game" I have to wonder if attempting to write a long-short strategy for Q would be a fool's errand. Personally, I don't work in the hedge fund world (or even in finance generally) and I don't have any acquaintances I could ask ("Hey Joe, how hard would it be to write a decent factor-based long-short strategy that scales to $10M in capital, using U.S. equities?"). So, I have no frame of reference.
You'll learn a lot by doing it yourselves, and then you can teach others. Q has some capable people. For example, let's say you, Fawce, Jess, Justin, and Thomas W. did nothing for two weeks but tried to come up with one or more factor-based long-short strategies? I think you'd gain credibility, learn the workflow first-hand, and potentially have an example algo you could share. Ignoring the opportunity cost, it might cost $50K to pay everyone, which is 0.5% of $10M--in relative terms, a small investment.
Also, I note on https://www.quantopian.com/about#op-28573-algorithm-writer-intern that you are actually looking to publish algos:
Quantopian is seeking an undergraduate intern to help expand our library of trading algorithms. We have dozens of algorithms that are described or otherwise implemented in languages other than Python. We would like these algorithms to be re-written in Python, tested in the Quantopian platform, and shared in the Quantopian community.
Presumably, these would be hedge fund style algos, since that's your business. So, I guess I'm confused about the idea that you wouldn't want to publish viable algos, since they'd be arbitraged away. What would the intern be doing then? Publishing algos that don't work?
On a separate note, you say "ranking schemes have predictive lifecycles" so is there code one could plunk into an algo, to flag when the strategy is working, and when it is spitting out jibberish? Or is the idea that one would manually run pyfolio and decide when the strategy has run out of gas?