I've found very interesting method of checking if a trading system "is broken". From the book of John Wolberg "Expert Trading System - Modeling Financial Markets with Kernel Regression" page 181â183. One of the most serious book on system design. (See attachment with links to relevant pages) I tried to implement his method, but faced with some uncertainties (Bearing in mind that my knowledge of statistics is not good as I would wish) First of all the author gives us daily ratio of σ/μ = 10 (Sigma/Mu = 10) My first question is where he gets it and what is statistical meaning of dividing standard deviation by mean? No matter, I can calculate stdev and mean from my equity curve. 1. I get difference of previous day equity and current day equity. 2. Calculate StDev and Average of it. My second question if is it correct ? The author still needs to find σ and μ, but I think it is not the same σ and μ as above because he calculates it trade days per year basis i.e. μ' = annual profit ^ (-250) - 1 . (daily μ') So I calculate μ' . After that I calculate daily σ' as 10 * μ' From his formula we can conclude that the less exponent in Eq. (2.13) the better result. Very logical - the less standard deviation and bigger average change in equity the better. However if I earn money with the system my equity get bigger and and consequently profit and drawdown is bigger. It increases StDev. And look at the formula 2μ/σ^2 -- Stdev in power of 2. It means the more money we make, the chance of drawdown grows aprox. in power of 2 and not linear.