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The is actually that absolutely nothing is at all unusual or significant concerning the fact that some average turned into the ideally. After all, we tested almost fifty industry experts against changing data, so we'd expect to find a few good performers, just unintentionally. It doesn't mean there's anything special about experienced moving average that "won" in scenario. The problem arises because we tested multiple hypotheses until discovered one that worked, rather than choosing unique hypothesis and testing this method.
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Notice that we can together with data during both the Observation and Experiment stages. So both views are right. We must use data to generate a sensible hypothesis, but we also test that hypothesis using data. The key is to simply make certain that the two sets of strategy are not the same! We must never test our hypothesis using related set of real information that we used to suggest our hypothesis. Some other words, if you use data mining in order to constructed strategy ideas, make sure you use a different associated with data to backtest those ideas.
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