Osmar Sandoval Espinosa:
I won't go deep into it since there's a ton of info out there you've probably already come across. But basically, "in-sample" data is what you use to train and shape the model, and "out-of-sample" data is what you use afterward to see if those learned patterns actually hold up in real-world conditions.
Recently I've been seeing a lot the terms in sample and out of sample, for what I have researched it refers to real market condition a backtest data.
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Recently I've been seeing a lot the terms in sample and out of sample, for what I have researched it refers to real market condition a backtest data.