Right now we are looking for algorithms that trade more frequently, turning over the portfolio value between a few times a day and say once a month at the very low end. The reason for this initial preference is that more frequently trading algos accumulate independent 'bets' or 'decisions' at a higher rate, so we need comparatively less out of sample data to gain confidence in the algorithm's behavior and profitability.
For a strategy that makes quarterly or fewer decisions a year you could easily argue that you'd need to see years of out of sample performance before you felt confident in attributing returns to skill as opposed to chance.
Setting the frequency question aside, you also asked about how consistent the performance should be to be considered 'good'. When I evaluate algorithms for allocations I am indeed looking for very consistent positive returns month over month and year over year.
I'll actually be giving a webinar on the topic of using the pyfolio tearsheet in our research platform to grade your own algorithms on Thursday of next week. Please join us or catch the recording after the fact.
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