{
  "schema": "canli.alphac-deflated-sharpe-calculator-contract.v1",
  "status": "REFERENCE_IMPLEMENTATION_CONTRACT",
  "version": "1.0.0",
  "published_on": "2026-08-26",
  "author": "Arhan Canli",
  "claim_boundary": "This contract reproduces ALPHAC's PSR and DSR arithmetic for supplied inputs. It does not validate a strategy, prove profitability, estimate current portfolio performance, or replace the complete admission contract.",
  "periodicity": {
    "formula_unit": "per_period_sharpe",
    "browser_input_unit": "annualized_sharpe",
    "observed_conversion": "sr_per_period = sr_annualized / sqrt(periods_per_year)",
    "dispersion_conversion": "variance_per_period = (sd_annualized / sqrt(periods_per_year))^2",
    "kurtosis": "non_excess; Gaussian equals 3"
  },
  "formula": {
    "psr": "Phi(((SR - SR*) * sqrt(T - 1)) / sqrt(1 - skew*SR + ((kurtosis - 1)/4)*SR^2))",
    "expected_max_sharpe": "sqrt(V[SR]) * ((1-gamma)*PPF(1-1/N) + gamma*PPF(1-1/(N*e)))",
    "dsr": "PSR(expected_max_sharpe)",
    "constants": {
      "euler_mascheroni": 0.5772156649,
      "e": 2.718281828459045
    }
  },
  "input_contract": {
    "observed_sharpe_annualized": {
      "minimum": -10,
      "maximum": 10
    },
    "observations": {
      "minimum": 2,
      "maximum": 1000000,
      "integer": true
    },
    "periods_per_year": {
      "minimum": 1,
      "maximum": 10000
    },
    "skew": {
      "minimum": -20,
      "maximum": 20
    },
    "non_excess_kurtosis": {
      "minimum": 1,
      "maximum": 100
    },
    "effective_independent_trials": {
      "minimum": 2,
      "maximum": 10000000,
      "integer": true
    },
    "cross_trial_sharpe_sd_annualized": {
      "minimum": 0,
      "maximum": 10
    }
  },
  "selection_accounting": {
    "trial_unit": "complete_union_hypothesis_identities",
    "variance_unit": "sample variance of per-period Sharpe across selection identities",
    "warning": "Reducing the trial count or dispersion after observing outcomes flatters DSR. The selection union must be defined independently of the selected result."
  },
  "current_policy": {
    "admission_schema": "canli.alphac-sleeve-admission-contract.v7",
    "per_sleeve_dsr": "mandatory_measurement_not_a_universal_gate",
    "incremental_admission": "not_decided_by_dsr_alone",
    "full_union_book_maturity_threshold": 0.95,
    "threshold_context": "The 0.95 threshold applies to a full-union portfolio-maturity claim. It is not a per-sleeve gate and a calculator result is not an admission verdict."
  },
  "source_bindings": {
    "implementation": {
      "path": "src/alphaforge/validation/dsr.py",
      "sha256": "sha256:ac2f18440e21b8241df4ac933748ab34125e42531aac2d1f5413aa4ad0deb1b2"
    },
    "admission_contract": {
      "path": "config/sleeve_admission_contract.json",
      "sha256": "sha256:6bc72c1f47675b4e14897e1ba3aad6e56aba9b5f013a7b9bae57f930e8d3b8b6"
    }
  },
  "references": [
    {
      "title": "The Deflated Sharpe Ratio: Correcting for Selection Bias, Backtest Overfitting, and Non-Normality",
      "authors": "David H. Bailey and Marcos López de Prado",
      "publication": "Journal of Portfolio Management 40(5), 94-107 (2014)",
      "url": "https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2460551",
      "doi": "10.2139/ssrn.2460551"
    }
  ],
  "test_vectors": [
    {
      "id": "daily_long_sample_heavy_tail",
      "inputs": {
        "observed_sharpe_annualized": 1.5,
        "observations": 730,
        "periods_per_year": 365,
        "skew": -0.5,
        "non_excess_kurtosis": 5.0,
        "effective_independent_trials": 229,
        "cross_trial_sharpe_sd_annualized": 0.57
      },
      "outputs": {
        "observed_sharpe_per_period": 0.07851358838853206,
        "cross_trial_sharpe_variance_per_period": 0.0008901369863013697,
        "expected_max_sharpe_per_period": 0.08382167718470863,
        "expected_max_sharpe_annualized": 1.6014108940590446,
        "psr_against_zero": 0.9809279924681973,
        "deflated_sharpe_ratio": 0.44426268122044366,
        "non_normality_variance_term": 1.04542117775591
      }
    },
    {
      "id": "trading_days_short_search",
      "inputs": {
        "observed_sharpe_annualized": 1.0,
        "observations": 252,
        "periods_per_year": 252,
        "skew": 0.0,
        "non_excess_kurtosis": 3.0,
        "effective_independent_trials": 10,
        "cross_trial_sharpe_sd_annualized": 0.5
      },
      "outputs": {
        "observed_sharpe_per_period": 0.0629940788348712,
        "cross_trial_sharpe_variance_per_period": 0.0009920634920634918,
        "expected_max_sharpe_per_period": 0.049595184764089716,
        "expected_max_sharpe_annualized": 0.7872991506724859,
        "psr_against_zero": 0.84062387995459,
        "deflated_sharpe_ratio": 0.5839730560867282,
        "non_normality_variance_term": 1.001984126984127
      }
    },
    {
      "id": "large_search_negative_skew",
      "inputs": {
        "observed_sharpe_annualized": 2.2,
        "observations": 1260,
        "periods_per_year": 252,
        "skew": -1.0,
        "non_excess_kurtosis": 9.0,
        "effective_independent_trials": 1000,
        "cross_trial_sharpe_sd_annualized": 0.75
      },
      "outputs": {
        "observed_sharpe_per_period": 0.13858697343671667,
        "cross_trial_sharpe_variance_per_period": 0.0022321428571428566,
        "expected_max_sharpe_per_period": 0.153790035936073,
        "expected_max_sharpe_annualized": 2.4413411352392136,
        "psr_against_zero": 0.9999970868543218,
        "deflated_sharpe_ratio": 0.3095140591397488,
        "non_normality_variance_term": 1.1769996718494151
      }
    }
  ],
  "content_hash": "sha256:241f33d627cecb876d5cd0cb0b9d48f18e20d55d921437ec2926d4603f92d349"
}