The probability of backtest overfitting

David H. Bailey, Jonathan M. Borwein, Marcos López de Prado, Qiji Jim Zhu · 2017 · The Journal of Computational Finance

IntermediateAny horizonMethodFree to read

Combinatorially symmetric cross-validation estimates the probability that your in-sample winner underperforms out of sample.

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At a glance

Level: Intermediate: Comfortable with statistics, regressions and basic probability models
Horizon: Any horizon
Data needed: None to read it; no dataset required Set what data you have
Themes: Backtesting
Assets: Equities
Market: Global

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Don't fool yourself: backtesting hygiene

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Cite

@article{bailey2017probability,
  author = {Bailey, David H. and Borwein, Jonathan M. and López de Prado, Marcos and Zhu, Qiji Jim},
  title = {{The probability of backtest overfitting}},
  journal = {The Journal of Computational Finance},
  year = {2017},
  doi = {10.21314/jcf.2016.322}
}

Bailey, D. H., Borwein, J. M., López de Prado, M., & Zhu, Q. J. (2017). The probability of backtest overfitting. The Journal of Computational Finance. https://doi.org/10.21314/jcf.2016.322