## Benchmarking backtest results against random strategies – robot wealth nzd usd exchange rate

There are a number of empirical methods that can be used to address this issue pre market oil futures. Chan describes three in his book mentioned above, and there are probably others __convert usd to zar__. I am going to implement the approach described by Lo, Mamaysky and Wang (2000), who simulated sets of trades constraining their quantity in each direction to be the same as in the backtest, and with the same average holding period and distributed randomly over the price series used in the backtest **cool pictures** to draw easy. These random strategies are run a large number of times and a frequency histogram of the performance metric of interest constructed global *market futures*. The strategy’s backtest performance is compared with this histogram to reveal insight into whether it is in fact better than random and does have predictive power.

I think that this method has real value when (and this is why I implemented it) the developer cherry picks a portfolio of strategies depending on their performance in an out of sample test.

For example, I optimized a strategy on a dozen different markets separately in the long and short directions live quotes. I then tested the portfolio of strategies on out of sample data and selected only those that performed well for the live portfolio euro **pound exchange rate history**. Using the proposed method of benchmarking in this scenario is essentially a counter for the selection bias introduced by cherry picking the strategies for the final portfolio.

This feels like a crude approach, but at the present time it is aligned with my level of programming skill nzd usd forecast. In order to use this on a random strategy with different assets, the lines extracted from the performance report would need to be modified accordingly british pound to *usd chart*. I do believe that Zorro can be programmaticly set to run a certain number of times using the NumTotalCycles parameter dollar euro exchange rate chart. The profit factor metric can also be calculated and stored in a histogram. I haven’t quite mastered this approach, but this would simplify things a great deal. I’ll update this post accordingly once I’ve gotten my head around this technique.

Lo, Andrew, Mamyskey, Harry and Wang, Jiang, 2000, Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation, Journal of Finance, Volume IV, Number 4 **yahoo finance futures market**. Post navigation

In your example application, you say that you pick the optimal strategy from a range of strategies based on out of sample performance. If this is the case, then you need to compare the statistics of this optimal strategy to the histogram of the ‘optimal’ profit factor.

A simple way to do this is on each of your 5000 runs, have the same number of random strategies running as in your original portfolio of strategies from which you choose from. Then for each of your 5000 runs, pick the optimal random strategy and record its profit factor. This is the correct distribution to compare your actual optimal strategy’s profit factor against.

See David Aronson’s 2011 textbook on technical analysis or look up White’s (2000) Reality Check method (see Hansen (2005) for an improved version). Reply

Thanks Emlyn! One limitation with the approach I documented above is that in order for it to work, one has to assume that no data mining bias has taken place. This would be almost impossible to achieve in reality.

If I understood you correctly, picking the optimal random strategy from each of the 5000 runs would be equivalent to Wihte’s bootstrap method? This seems like a much simpler implementation than keeping track of all the tested strategy variants and detrending and sampling from their equity curves, as is my understanding of White’s Reality Check.

Even if it is not precisely equivalent, I can see that your suggestion is a more robust benchmark to use than the one I wrote about. Thanks for sharing it! Reply