Many thanks. Two quick questions please: 1. What are your preferred and ideal values for the Average Return/Average Drawdown ratio. 2. Please confirm that the paper you refer to is the one at this link: http://www.nextdsystems.com/resources/NextDimension Drawdowns.pdf
Thanks, MM! Very insightful paper. Will surely reread it. Of course the higher the better...but what are your preferred/ideal levels? What do you typcially see in your experience? Finally how can one get access to the testing software refeernced in that paper. I would surely like to use it.
I suggest reading The Way of The Turtle by Curtis Faith. The book answers your question in detail. For example, net profit alone is not a good indicator since most of the profit could have come from a single trade. Also, High Probability Trading my Marcel Link has an excellent section on strategy development and backtesting. Asking this on ET will only get you a thousand different opinions and will confuse you. The two authors above give good explanations of what to look for.
Thank you. (I am the author of that paper, by the way.) The levels seen can vary widely and depend on many factors. The testing software is software that I wrote specifically for the purpose of developing and testing my trading models. The software itself isn't commercially available, but the algorithm is part of a 3-day workshop on system development that is being given at the end of October. Building this tool should be no sweat after that. M
Unfortunately, you can't run unlimited simulations over trading system backtests. Resampling techniques (bootstrap, jackknife, etc...) introduce complexities of bias and variance beyond the ken of most ET'ers. For this reason, historital max or average drawdown is not a good estimator of future max or average drawdown. I am sure you are aware of this. I am not sure that you know, however, that for normal or near normal distributions of model returns, historical volatility and mean return yeild better estimates. For the case of zero mean return (no alpha, the case with most models discussed here) the formula ifor expected max drawdown is simple: 1.25 * Stdev * sqrt(Time). For the positive mean return the estimate gets a little more complex: (2 * QP((Time / 2) * ((MeanReturn / Stdev)^2)) / MeanReturn The QP function, AFAIK, does not have an analytical solution. You can download a lookup table for it, however, at this website: http://www.cs.rpi.edu/~magdon/data/Qp.txt Interestingly, for a given Sharpe Ratio this works out to a linear relationship scaling with the square root of time. For example, for the Hershey method, with a claimed daily basis annualized Sharpe Ratio of 5.5, the equation is 0.50 * Stdev * sqrt(Time) To sum up, the inforation in the "new" ratio under discussion is implicit in the Sharpe Ratio itself.
Excellent discussion, Kevin. Unfortunately, the models that I work with tend to have nowhere near a normal distribution of returns, and I suspect this is true for others as well. Historical average drawdown does work very well both in theory and in practice to the extent that the historical return series is unbiased (using a biased input series is the biggest problem with nearly all analysis done by system developers) and the underlying assumptions of any statistical procedures are satisfied. The assumptions are nearly always violated to some extent, and this must be considered when interpreting output. M
Kevin, Do you have a reference for the formulas you provide above. I'd like to look into this a bit more.
Backtesting of a system is more or less useless. Real time trading results you will get from a system will be about 20% below the results you get from backtesting. When a chart is forming it looks quite different from completed chart and you canât simulate that in backtesting. For example, system I presently trade will give me close to 100% winners/losers ratio in backtesting, in real time I get about 80%.