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Financial data is usually structured with time bars. Other sampling techniques include:

  • tick bars
  • volume bars
  • dollar bars.

These are so-called sampling techniques to better identify signals and trends in financial data.

But if they are sampling techniques, then how do they compare to sampling techniques used to test the robustness of a model, such as cross-validation and bootstrapping/bagging (bootstrap aggregation)? Or are they incomparable and completely different animals? I'm thinking that bars are simply aggregation techniques, and are not higher-level sampling methods like CV and BS. Am I right to differentiate between sampling and aggregation and that they are different things?

With this categorization aside, are there any issues one must consider when cross-validating or bagging with tick, volume, or dollar bars vs. time bars?

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Your thinking is correct: bars are simply for aggregation. Within the realm of aperiodic data (tick data), bars make the data periodic, which in turn makes certain types of analysis easier to perform. I wouldn't call bars a "sampling" technique since that implies random selection.

Cross-validation checks a predictive model's sensitivity to overfitting by using random subsets or partitions of the data. Bootstrapping assesses the accuracy of a statistic by using random drawings (with replacement) of the data.

I can't imagine there would be any gotchas for using different definitions of bars as inputs for cross-validation or bootstrap.

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