How did you approximate a 15 percent return with 20 percent max drawdown equating to a Sharpe of 1.5?
If you bootstrap random data you find the max drawdown is roughly twice the annualised standard deviation of returns (see my post here). So we've got: max drawdown=20% implies ann. std. dev= 20% * .5 = 10% SR = 15% / 10% = 1.5 GAT
My guess is in realily, it would be very unusual for a 15% return system to get a SR 1.5 with 20% MaxDD. Just 2 cents!
Well if the returns were drawn from a gaussian distribution (which is an assumption you can argue about, if you like) then on average that's exactly what you'd see. Unless you're saying that the SR of 1.5 is unusual; I'd agree this is highly optimistic also in my opinion. http://www.risk.net/data/Pay_per_view/risk/technical/2004/1004_tech_atiya.pdf GAT
Perhaps an easier thing to quantify is not the success, but the reciprocal of it, the failure, and more specifically, the probability of the failure to produce return above 0, or above some other reference return. The lower that probability, the lower the risk of failure, and thus the higher the success. From this perspective, I like the Stutzer index. It has some desirable properties: -- for normally distributed returns, it equals to Sharpe's ratio -- unlike the Sharpe's ratio, it does not penalize upside volatility (i.e. abnormally high returns) -- unlike the Sharpe's ratio, it is appropriate for non-normally distributed returns -- unlike the Sharpe's ratio, it does not violate the stochastic dominance (or in plain English, it does not produce nonsensical results) Here is Stutzer's original paper: http://www.yats.com/downloads/StutzerIndex.pdf