Simple question - what would be the fastest algorithm for calculating retrospective maximum drawdown ? I've found some interesting talks but I was wondering what people thought of this question here.
Let $$ X_t = \mu t + \sigma B_t $$ be a linear Brownian motion with drift. Let $$ S_t = \max(X_u, u \le t) $$ denote the process of the running max, then the draw down is given by $$ DD_t = S_t - ...
When evaluating the performance of an algorithm, what should hold more importance? Sharpe Ratio , Net profit or max drawdown? For instance, I have two algorithms one performs very good on Stocks with ...
Can anyone tell me whether results as predicted by Brownian Motion for a given mean and std, match what you get by measuring actual drawdown from simulated results over a number of iterations?