The Elite Circle - Bootstrapped Monte Carlo Expectancy for Robust Optimization in NT7
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Bootstrapped Monte Carlo Expectancy for Robust Optimization in NT7 5 *
This algorithm is a custom performance index for the Default or Genetic optimization methods in NinjaTrader 7. Optimization with this index maximizes the 20th percentile of noise-injected bootstrapped expectancy. This metric is designed for robustness against worst case conditions. Note that due to the mathematics, the solution is not deterministic (run it multiple times, and you will get slightly different solutions each time).

Add this file to your ...\NinjaTrader 7\bin\Custom\Type\ directory, open any indicator or strategy, and compile.

Details (this math is in here):
1. For a given series of trades of length N, the top 10% are removed, creating a modified series of trades of length N*0.9.
2. This modified series of trades is sampled *with replacement* for 10*N*0.9 samples. This series becomes the bootstrapped trade series.
3. Gaussian noise is a given mean and standard deviation is added to each bootstrapped sample.
4. Expectancy for this bootstrapped + noise series is calculated.
5. Steps 2 through 4 are done 5000 times, with different sampling from the Gaussian noise. This creates a distribution of expectancy.
6. Finally, the performance index returns the 20th percentile of these 5000 runs (so, the 1000th worst expectancy) to the NT optimizer.

Removal of the 10% best performing trades is an attempt to be pessimistic by assuming that the best 10% trades observed in the past will never repeat. We do not want total system performance to hang a few high-yielding trades.

Injecting noise to the bootstrapped sample accomplishes two things. First, it adds uncertainty to each bootstrapped series, thereby simulating diversity in the bootstrapped series (hopefully the diversity we would measure in a much larger sample). Secondly, it makes each bootstrap series unique. This means that we way calculate a distribution from the ensemble of bootstrapped series.

Any percentile may be returned. We could return the mean. By returning a value to the left of the mean, the 20th percentile, we are maximizing 80% of the distribution.

Note: Because we are injecting randomly sampled noise to the bootstrapped series, the solution from this performance index is not deterministic!

If you use this, you should be comfortable changing the parameters found at the top of the algorithm. Adjusting the value of the standard deviation of the noise may make or break the robustness and success of this algorithm.

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