{
  "schema": "canli.alphac-backtest-overfitting-calculator-contract.v1",
  "status": "REFERENCE_IMPLEMENTATION_CONTRACT",
  "author": "Arhan Canli",
  "published_on": "2026-09-06",
  "claim_boundary": "This runs the production Combinatorially Symmetric Cross-Validation implementation on whatever variant returns you submit. It does not know where your matrix came from, cannot detect lookahead in how it was built, and a low probability of overfitting is not proof a strategy is deployable. It is one diagnostic about whether an in-sample ranking predicts an out-of-sample one, nothing more.",
  "method": {
    "name": "Combinatorially Symmetric Cross-Validation (CSCV)",
    "reference": {
      "authors": "Bailey, Borwein, Lopez de Prado and Zhu",
      "year": 2017,
      "title": "The Probability of Backtest Overfitting",
      "publication": "Journal of Computational Finance 20(4), 39 to 69"
    },
    "question": "Given many configurations evaluated on the same sample, does the one that ranks best in-sample also rank well out-of-sample, across every symmetric way of splitting the sample in half?",
    "steps": [
      "Split the T rows into S contiguous equal blocks (S even). Drop any remainder rows so every block has exactly T div S rows.",
      "For each way of choosing S/2 blocks as in-sample, form the in-sample matrix from those blocks and the out-of-sample matrix from the rest.",
      "Take the in-sample winner: the configuration with the highest in-sample Sharpe.",
      "Find that same configuration's rank among all configurations out-of-sample, ascending, and take the logit of its relative rank.",
      "PBO is the fraction of splits where the in-sample winner lands in the worse half out-of-sample."
    ]
  },
  "implementation": {
    "path": "js/pbo-core.js",
    "matches_engine": "a line-for-line port of alphaforge.validation.pbo.pbo_cscv, the same core the validation API's POST /api/v1/validate/overfitting route imports, so a browser result and an API result on the same matrix agree",
    "exhaustive_rule": "when C(n_splits, n_splits / 2) is at most the combinations ceiling, every split is enumerated and the result is bit-comparable to the Python reference. Above that ceiling, n_splits / 2 sized subsets are sampled without replacement by this repo's mulberry32 generator, and the result is reported as exhaustive: false, agreeing with the Python reference within sampling noise rather than bit for bit."
  },
  "defaults": {
    "n_splits": 16,
    "max_combinations": 2000,
    "seed": 42
  },
  "worked_example": {
    "vector_id": "exhaustive_8x4_70combos",
    "source": "standards/validation-api/vectors.json, the golden-vector corpus the validation API is tested against",
    "n_splits": 8,
    "max_combinations": 2000,
    "seed": 42,
    "n_rows": 160,
    "n_configs": 8,
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      "n_combinations": 70,
      "exhaustive": true,
      "block_length": 20
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  },
  "sandbox_safety": [
    "the matrix and parameters you enter never leave your browser; nothing is uploaded",
    "no write path to any ledger, artifact, broker or published record exists",
    "export downloads a JSON receipt of exactly what you entered and what it computed"
  ],
  "content_hash": "sha256:2b17a58d17ef9cf90a8c72017bad0cd2b9ef905743ad77177bb1afb77654fbe0"
}