The usual response to this phenomenon is to conduct testing with out of sample data. In other words designing the system on one data set and refining it to a certain degree and then testing it on a completely new set of market data to see if the edge persists. If you were systems development practice find you always discarding your systems after the out-of-sample test, it probably occurs as a result of over optimization.

In practice then, we want to find the trade-off between robustness of performance in multiple market conditions with out-of-sample testing that yields a persistent advantage in multiple market conditions but without trying to overturn the system for Telephone Number List ideal conditions. A way to keep this systematic approach in tune is to continue to monitor performance in a feed-forward approach that examines actual trading results to see how the performance results compare to the test and confirmation performance curves.

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