{
  "contract": "systematic-research-source-kit-v1",
  "publishedAt": "2026-09-16",
  "scope": "Citation and reuse metadata for reviewed Grizzly Bulls systematic-trading studies that retain canonicals outside the flagship /research catalog.",
  "studies": [
    {
      "id": "backtest-selection-bias",
      "path": "/backtest-selection-bias",
      "canonicalUrl": "https://grizzlybulls.com/backtest-selection-bias",
      "title": "How Good Can a Zero-Edge Backtest Look After Strategy Search?",
      "question": "If every tested strategy has zero true expected return, how impressive can the best reported backtest look purely because the research process selected the winner from many trials?",
      "keyFinding": "With 252 daily observations and 100 independent zero-edge strategy trials, the median winning annualized Sharpe is about 2.48 and the probability that the winner reaches Sharpe 2 is about 90.52%.",
      "assumptions": "Independent strategy trials; independent Normal daily returns; zero true expected return; 252 annualization periods.",
      "limitation": "Real strategy variants are usually correlated, so the benchmark does not estimate the false-positive probability of a particular real research process.",
      "publishedAt": "2026-09-15",
      "citation": "Bailey, Lee. “How Good Can a Zero-Edge Backtest Look After Strategy Search?” Grizzly Bulls, September 15, 2026. https://grizzlybulls.com/backtest-selection-bias",
      "downloads": {"csv": "/research-data/backtest-selection-bias-2026-09-15.csv", "json": "/research-data/backtest-selection-bias-2026-09-15.json"},
      "methodologyPath": "src/lib/backtestSelectionBias.ts"
    },
    {
      "id": "backtest-out-of-sample-decay",
      "path": "/backtest-out-of-sample-decay",
      "canonicalUrl": "https://grizzlybulls.com/backtest-out-of-sample-decay",
      "title": "What Happens Out of Sample After You Select the Best Backtest?",
      "question": "After selecting the strongest in-sample backtest from many zero-edge candidates, what does that selected strategy look like on a genuinely independent holdout when the underlying process has not changed?",
      "keyFinding": "After selecting the best of 100 one-year zero-edge backtests, the median selected in-sample Sharpe is about 2.48 while the independent one-year holdout median is 0; only about 0.69% of holdouts match or exceed that selected in-sample median.",
      "assumptions": "Independent strategy trials in sample; independent Normal returns; zero true expected return; genuinely untouched independent holdout.",
      "limitation": "Reusing the holdout for further model selection makes it part of the research search, so this benchmark does not claim one train/test split solves backtest overfitting.",
      "publishedAt": "2026-09-15",
      "citation": "Bailey, Lee. “What Happens Out of Sample After You Select the Best Backtest?” Grizzly Bulls, September 15, 2026. https://grizzlybulls.com/backtest-out-of-sample-decay",
      "downloads": {"csv": "/research-data/backtest-out-of-sample-null-2026-09-15.csv", "json": "/research-data/backtest-out-of-sample-null-2026-09-15.json"},
      "methodologyPath": "src/lib/backtestOutOfSampleNull.ts"
    },
    {
      "id": "backtest-sharpe-significance-threshold",
      "path": "/backtest-sharpe-significance-threshold",
      "canonicalUrl": "https://grizzlybulls.com/backtest-sharpe-significance-threshold",
      "title": "How High Should Sharpe Be After You Search Many Strategies?",
      "question": "After testing many independent zero-edge strategies, how high must the winning annualized Sharpe be for the entire research search to cross a chosen family-wise false-positive threshold?",
      "keyFinding": "At a 5% family-wise error rate, 252 daily observations and 100 independent zero-edge strategy trials require an annualized winning Sharpe of about 3.32, versus about 1.65 for one pre-specified strategy.",
      "assumptions": "Independent strategy trials; independent Normal returns; zero true expected return; one-sided family-wise tail probability; 252 annualization periods.",
      "limitation": "Real strategy variants are usually correlated and adaptive research can make the effective search breadth difficult to count, so the benchmark does not infer an effective number of independent trials from a real parameter sweep.",
      "publishedAt": "2026-09-15",
      "citation": "Bailey, Lee. “How High Should Sharpe Be After You Search Many Strategies?” Grizzly Bulls, September 15, 2026. https://grizzlybulls.com/backtest-sharpe-significance-threshold",
      "downloads": {"csv": "/research-data/backtest-sharpe-significance-threshold-2026-09-15.csv", "json": "/research-data/backtest-sharpe-significance-threshold-2026-09-15.json"},
      "methodologyPath": "src/lib/backtestSharpeSignificanceThreshold.ts"
    },
    {
      "id": "backtest-correlated-strategy-search",
      "path": "/backtest-correlated-strategy-search",
      "canonicalUrl": "https://grizzlybulls.com/backtest-correlated-strategy-search",
      "title": "How Much Does Correlation Reduce Backtest Selection Bias?",
      "question": "How does dependence among many zero-edge strategy trials change the null distribution of the selected winning Sharpe ratio?",
      "keyFinding": "With 252 observations and 100 trials, the median winning Sharpe is about 2.48 under independence, about 1.78 at 0.50 latent dependence, and about 0.79 at 0.90 latent dependence in the one-factor Gaussian-copula benchmark.",
      "assumptions": "Student-t finite-sample Sharpe marginal from the KT19 zero-edge null; one-factor Gaussian copula; common latent correlation across trials; 252 annualization periods.",
      "limitation": "The copula parameter is a simplified dependence input, not a direct estimate of live strategy-return correlation and not a universal mapping to an effective number of independent trials.",
      "publishedAt": "2026-09-16",
      "citation": "Bailey, Lee. “How Much Does Correlation Reduce Backtest Selection Bias?” Grizzly Bulls, September 16, 2026. https://grizzlybulls.com/backtest-correlated-strategy-search",
      "downloads": {"csv": "/research-data/backtest-correlated-strategy-search-2026-09-16.csv", "json": "/research-data/backtest-correlated-strategy-search-2026-09-16.json"},
      "methodologyPath": "src/lib/backtestCorrelatedStrategySearch.ts"
    }
  ],
  "reuseGuidance": [
    "Cite the stable canonical study URL.",
    "Preserve the study assumptions and any limitation that materially changes interpretation of a reused statistic.",
    "Public aggregate CSV and JSON files may be used to verify calculations or build new charts with attribution to Grizzly Bulls and the canonical study.",
    "This manifest is a citation aid, not a substitute for each study's full methodology and limitations."
  ],
  "authority": {"publicationAuthority": false, "researchMutationAuthority": false, "outreachAuthority": false, "citationObservationAuthority": false}
}