Quote from nonlinear5:
I thought about this for quite a while even before this discussion has started. It certainly makes sense to include the length of the trading history in the ranking equation. However, I think that the number of trades is equally (if not more) important than the length of the history.
Consider two trading strategies, A and B.
Strategy A has a history of 10 years, and made 10 trades.
Strategy B has a history of 1 year, and made 100 trades.
Everything else being equal (i.e, all the other performance metrics are the same), which one do you trade? According to your RAPA score, and assuming that you use the term sqrt(history length), strategy A will get the score which would be 3.16 times higher than that of strategy B. But is A really 3.16 times more attractive? I'd say "no". The sample size of 10 trades is too low, and thus the probability that strategy A accomplished its performance simply by chance is too high.
I'd say that if you take into consideration both the length of the trading history and the number of trades, it would be more fair:
Strategy A: sqrt(10) * sqrt(10) = 10
Strategy B: sqrt(1) * sqrt(100) = 10
What do you think?
We are doing it differently: we do not have a sqrt(number of observation) multiplier but given different trading histories the distribution of Sharpe ratios is different. The typical situation is that A and B can have the same Sharpe ratio but since A has longer history it is being ranked based on a distribution with thinner tails. The classical paper that is dealing with this kind of situation is http://papers.ssrn.com/sol3/papers.cfmbstract_id=377260
Concerning the trades: there are alot of managers (and on our platform as well) who is using a buy and hold strategy - it does not have a lot of trades but in my opinion this strategy is the easiest to assess.