Purged Cross-Validation & Embargo Calculator

Ordinary time-series splits can still leak when labels use future observations. This tool makes the boundary mechanical: purge training rows whose label-information windows overlap validation, then embargo a further post-validation buffer.
100 rows, 5-row label horizonValidation rows 40–59 carry information through row 64.
5-row embargoThe reference case purges 10 training rows, embargoes 5 more, and retains 65 training rows.

Validation geometry

Indices are zero-based and the validation window is inclusive. A label horizon of 5 means row i uses information through row i+5, capped at the sample end.

Leakage-aware split

20Validation rows
40–64Validation information interval
10Training rows removed for label overlap
5Additional post-validation embargo rows
65Retained training rows
81.3%Retained share of non-validation rows

Purging protects the label boundary

A row can sit outside the validation fold and still leak validation information if its target is computed from a forward window that reaches into the validation period. Purging removes every candidate training row whose label-information interval overlaps the union of the validation labels' information intervals.

For a training interval [t0, t1] and validation information interval [T0, T1], this implementation treats them as overlapping when t0 ≤ T1 and t1 ≥ T0. That is the mechanical leakage condition this calculator exposes.

Embargo adds a second buffer

Purging removes direct label overlap. Embargo removes an additional block of candidate training observations immediately after the validation information interval. The purpose is different: it creates extra temporal separation where nearby observations or features may remain serially dependent even after direct label overlap is gone.

The correct embargo is research-design dependent. This tool accepts an explicit row count rather than claiming one percentage is universally sufficient.

What this check does not prove

Purging and embargo reduce a specific form of validation leakage. They do not make observations IID, repair look-ahead features, fix survivorship bias, reconstruct missing point-in-time data, account for strategy search multiplicity, or create genuinely unseen future data.

Use the PBO / CSCV calculator to test the selection process across complementary splits, the autocorrelation check for serial dependence inside returns, and the Deflated Sharpe Ratio for selection-adjusted Sharpe evidence.

Reusable reference case

The checked-in reference case uses 100 observations, a 5-row forward label horizon, validation rows 40–59, and a 5-row embargo. It retains 65 training rows after removing 10 overlap rows and 5 embargo rows.

Download the reference case JSON

Method sources

  • Marcos López de Prado, Advances in Financial Machine Learning, Chapter 7, formalizes purging overlapping label intervals and embargoing observations after the test set.
  • Financial ML Core: PurgedKFold documents the interval-overlap rule used to remove training labels that intersect the test interval.