future of Q may just depend on this. We would not consider using any system that had not made this sort of consideration of tail risk, and for a professional fund not to do this, especially when it is so easy, would be bordering on downright negligent! The big advantage of running a MC simulation using the results from an actual 6 month period of trading is not just that it gives a "best estimate" of the likely results from the next (i.e. Start adding in MC evaluations of tail risk based on the distribution of trade returns from all algos asap. As for the return part, that's fairly easy. This site is operated by a business or businesses owned by Informa PLC and all copyright resides with them. The latter choice makes more sense to me, as it highlights the combination of reward AND risk together. Now, assuming that Q actually wants algos that are durable and robust under different market conditions in future, is there anything that Q can do to improve the chances of success in future, rather than just simply running a live test for 6 months? 1 rebalancing : Some algos are reasonably sensitive to whether rebalancing is done daily, weekly, monthly, or at some other interval(s). It is common nowadays in most commercial trading software packages (e.g.
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This of course makes perfect sense because, after specifying satisfying various constraints, such as whatever Q may need to impose as requirements for the fund, and also to facilitate evaluation of the algos, some combination of the two metrics return volatility are really all that. The order of a sequence of gains losses can be changed, but the final return remains the same. What I find absolutely incredible is that Q is putting so much effort into encouraging people to write good algos with supposedly leading edge "Risk" evaluation tools, but is apparently doing nothing at all with regard to quantifying even the basic elements of this different. I ask Q please to give this part very careful thought. Finally on this point, there is the question of whether or not Q is going to continue making a single (1-dimensional) ranking of participants in contests. Future) 6 months or whatever period of trading, but also it generates a probability distribution for the possible trading results that allows quantification of the Black Swan type "tail risk" (to Quantopian) of continuing to use this algo in future (at least to the best. Personally I would suggest the latter, as it is the best way of also taking into consideration the time element of the DD as well as its magnitude. Assumptions i) ii) may turn out to be wrong, but they are about as good as anyone can do in advance and are consistent with statistical theory as well as common sense.
I have to wonder why not, when it is so easy to implement? Please Q, for the sake of your and our future, if you are not implementing this already, then start doing it now AND include it in the final evaluation of all algos, even if not (which would be easy enough) as an ongoing item while.
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