Hello Dan -
Thanks for the feedback regarding daily VWAP. I knew about your use of NxCore for live trading: as I understand, you take in trades from NxCore, and output minute bars real-time. I was not aware that you have the NxCore trades back to 2002, correct? If so, I see that you could do a true VWAP calculation using those data, and update it real-time by modifying your "injestor" code (the "real-time" code that takes in trades and spits out OHLCV minute bars). For intraday trading, I can understand that you'd want to do the VWAP computation in this fashion. However, for pipeline, which operates on trailing daily data, it would seem that the sum(V*(H+L+C)/3) (over minute bars) would suffice to provide a decent representation of the price for the day, and would be easy to do (with no modification to your real-time injestor). It just seems like a much more tractable project than computing a true VWAP, supplied as a minutely feed. And naively, I would think that for the kind of pipeline-based long-short workflow you are promoting, from a practical standpoint, the VWAP estimated from minute bars would be equivalent to a true VWAP computed from individual trades.
The issue of the requirements for an allocation and providing feedback to users is important, I think. The broad requirements are fine, I suppose, and probably pretty vanilla for the industry. The workflow makes sense. As you've explained, all algos with backtests in your system are evaluated, and then passed on to your R&D team, if certain criteria are met. Then, they are looked at more closely, including their correlation with other algos you are considering. However, in all of this, no feedback flows back to the author, unless more information is requested from the author to incorporate into your decision-making process (which was standard practice awhile back), which would give the author a clue that his algo may have some merit. From a user perspective, the process is a total black hole. Basically, all one can do is wait 6 months for an e-mail from Quantopian, and if one doesn't come, assume that the algo was passed over--there is no feedback.
While I appreciate that you need to cobble together a fund that is more than the sum of its parts (by selecting uncorrelated return streams), from an author perspective, the "Low Correlation to Peers" requirement is difficult, if impossible, to assess. Perhaps for seasoned industry insiders, it is known where to look (and not to look) for sources of untapped, uncorrelated alpha, but for novices, it is kinda ill-defined and daunting. This requirement is coupled with the "Strategic Intent" one which suggests that an author will be expected to explain his strategy, providing a kind of theoretical basis (assuming that an scalable, uncorrelated strategy was found, that meets the risk management criteria, as well). You are asking for a lot, and without a process for definite, specific feedback to users, it feels like more of a game of chance than an R&D effort.
The other issue is that the requirements have been changing, pretty much constantly, since the fund concept was introduced in fall 2014 (I see that you've fleshed them out a bit recently). Presumably, if one writes an algo today, it will be judged on the requirements 6 months from now, and not on the requirements when it was written. Presumably the requirements are stabilizing, but history suggests that they could be a bit of a moving target.
I have to think that for quants working within a traditional hedge fund, there is some sort of feedback within the R&D cycle. One would not just put up a bunch of requirements, provide some tools, and not provide feedback to the R&D team.
Perhaps my sentiment on the fund requirements and feedback is unique. It would be interesting to hear other constructive perspectives.